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Energy-Effective Communication Based on Compressed Sensing

Received: 5 November 2016     Accepted: 22 November 2016     Published: 8 December 2016
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Abstract

In order to improve energy-effect, Compressed Sensing has been employed gradually in the process of collaborative communication. For practical applications, localized features of local area are further considered in this technology, which is called classification CS. However, collaborative methods in current literatures are not so suitable in this scene that the advantages of CS could not be benefit. In this paper, a novel collaborative communication mechanism based on classification CS is proposed for actual environments. An effective collaborative transmission mode based on classification is presented, in which energy cost reduce effectively in the process of transmission and the reconstructed signals could reach at least the theoretical low bound to avoid redundant samplings. In experiments, our mechanism has been proved valuable and feasible in realistic applications.

Published in American Journal of Networks and Communications (Volume 5, Issue 6)
DOI 10.11648/j.ajnc.20160506.11
Page(s) 121-127
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Collaborative Routing, Compressed Sensing, Communication, WSN

References
[1] J. Haldar, and D. Hernando, and ZP Liang, “Compressed Sensing in MRI with non-Fourier encoding”, IEEE Trans. Med. Imag, vol. 30, pp. 893–903, 2013.
[2] J. Wu, and F. Liu, and LC Jiao, and X. Wang, “Compressive Sensing SAR Image Reconstruction Based on Bayesian Framework and Evolutionary Computation”, IEEE Trans. Image Processing, no. 99, pp. 1-1, 2014.
[3] H. Yang, K. Tang, et al, “An adaptable CS-based transmission scheme validated on the real-world system,” IEEE INFOCOM 2016
[4] Z. Charbiwala, S. Chakraborty, S. Zahedi, Y. Kim, M. B. Srivastava, T. He and C. Bisdikian. Compressive Oversampling for Robust Data Transmission in Sensor Networks. In INFOCOM, 2010.
[5] H. Yang, et al, “A Practical Information Coverage Approach in Wireless Sensor Network,” Information Processing Letters, vol. 115, no. 1, pp. 6-10, 2015.
[6] C. Feng, W. S. A. Au, et al. Compressive sensing based positioning using RSS of WLAN access points. In INFOCOM, 2010.
[7] PS. C. Thejaswi, T. Tran, and J. Zhang. When compressive sampling meets multicast: Outage analysis and subblock network coding. In INFOCOM, 2011.
[8] A. Wani, etc. Compressive Sampling for Energy Efficient and Loss Resilient Camera Sensor Networks. In INFOCOM, 2011.
[9] Z. Li, Y. Zhu, H. Zhu, and M. Li. Compressive Sensing Approach to Urban Traffic Sensing. In ICDCS, 2014.
[10] H. Yang, et al, “Distributed Compressed Sensing in Wireless Local Area Networks,” International Journal of Communication Systems, vol. 22, no. 11, pp. 2723-2743, 2014.
[11] H. Yang, L. Huang, H. Xu, “Distributed Compressed Sensing in Vehicular Ad-hoc Network,” Ad hoc & Sensor Wireless Networks, vol. 25, no. 1-2, pp. 121-145, 2015.
[12] E. J. Cand`es, J. Romberg, and T. Tao, “Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information”, IEEE Trans. Inform. Theory, vol. 52, no. 2, pp. 489-509, 2006.
[13] David L. Donoho, “Compressed sensing”, IEEE Trans. on Inform. Theory, vol. 52, no. 4, pp. 1289-1306, 2006.
[14] E. Candes, “The Restricted Isometry Property and its Implications for Compressed Sensing”, Comptes Rendus Mathematique, vol. 346, no. 9. pp. 589-592, 2008.
[15] M. F. Duarte, and S. Sarvotham, and D. Baron, and M. B. Wakin, and R. G. Baraniuk, “Distributed Compressed Sensing of Jointly Sparse Signals”, Asilomar Conf. Signals, Sys., Comput, pp. 1537-1541, 2005.
Cite This Article
  • APA Style

    Wang Yin-yin. (2016). Energy-Effective Communication Based on Compressed Sensing. American Journal of Networks and Communications, 5(6), 121-127. https://doi.org/10.11648/j.ajnc.20160506.11

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    ACS Style

    Wang Yin-yin. Energy-Effective Communication Based on Compressed Sensing. Am. J. Netw. Commun. 2016, 5(6), 121-127. doi: 10.11648/j.ajnc.20160506.11

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    AMA Style

    Wang Yin-yin. Energy-Effective Communication Based on Compressed Sensing. Am J Netw Commun. 2016;5(6):121-127. doi: 10.11648/j.ajnc.20160506.11

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  • @article{10.11648/j.ajnc.20160506.11,
      author = {Wang Yin-yin},
      title = {Energy-Effective Communication Based on Compressed Sensing},
      journal = {American Journal of Networks and Communications},
      volume = {5},
      number = {6},
      pages = {121-127},
      doi = {10.11648/j.ajnc.20160506.11},
      url = {https://doi.org/10.11648/j.ajnc.20160506.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnc.20160506.11},
      abstract = {In order to improve energy-effect, Compressed Sensing has been employed gradually in the process of collaborative communication. For practical applications, localized features of local area are further considered in this technology, which is called classification CS. However, collaborative methods in current literatures are not so suitable in this scene that the advantages of CS could not be benefit. In this paper, a novel collaborative communication mechanism based on classification CS is proposed for actual environments. An effective collaborative transmission mode based on classification is presented, in which energy cost reduce effectively in the process of transmission and the reconstructed signals could reach at least the theoretical low bound to avoid redundant samplings. In experiments, our mechanism has been proved valuable and feasible in realistic applications.},
     year = {2016}
    }
    

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    Y1  - 2016/12/08
    PY  - 2016
    N1  - https://doi.org/10.11648/j.ajnc.20160506.11
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    T2  - American Journal of Networks and Communications
    JF  - American Journal of Networks and Communications
    JO  - American Journal of Networks and Communications
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    AB  - In order to improve energy-effect, Compressed Sensing has been employed gradually in the process of collaborative communication. For practical applications, localized features of local area are further considered in this technology, which is called classification CS. However, collaborative methods in current literatures are not so suitable in this scene that the advantages of CS could not be benefit. In this paper, a novel collaborative communication mechanism based on classification CS is proposed for actual environments. An effective collaborative transmission mode based on classification is presented, in which energy cost reduce effectively in the process of transmission and the reconstructed signals could reach at least the theoretical low bound to avoid redundant samplings. In experiments, our mechanism has been proved valuable and feasible in realistic applications.
    VL  - 5
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    ER  - 

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Author Information
  • The First People’s Hospital of Yancheng, Yancheng, P. R. China

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